Development of a vibration measurement device based on a MEMS accelerometer

© 2017 by SCITEPRESS. Published under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International licence (CC BY-NC-ND 4.0: https://creativecommons.org/licenses/by-nc-nd/4.0/)This paper proposes a portable and low cost vibration detection device. Enhanced vibration calculation, reduction of error and low storage memory are complementary accomplishments of this research. The device consists of a MEMS capacitive accelerometer sensor and microcontroller unit, which operates based on a novel algorithm designed to obtained vibration velocity, bypassing the usual time-based integration process. The proposed algorithm can detect vibrations within 15Hz-1000Hz frequencies. Vibration in this frequency range cannot be easily and accurately evaluated with conventional low cost digital sensors. The proposed technique is assessed and validated by comparing results with an industrial grade vibration meter

Auto-sapiens autonomous driving vehicle

This paper presents the Auto-Sapiens project, an autonomous driving car developed by the Mechatronics and Vehicle Dynamics Lab, at Sapienza University of Rome. Auto-Sapiens is a technological platform to test and improve innovative control algorithms. The car platform is a standard car (Smart ForTwo) equipped with throttle, brake, steering actuators and different sensors for attitude identification and environment reconstruction. The first experiments of the Auto-Sapiens car test a new obstacle avoidance. The vehicle, controlled by an optimal variational feedback control, recently developed by the authors, includes the nonlinearities inherent in the car dynamics for better performances. Results show the effectiveness of the system in terms of safety and robustness of the avoidance maneuvers

On the road with third-party apps: Security analysis of an in-vehicle app platform

Digitalization has revolutionized the automotive industry. Modern cars are equipped with powerful Internetconnected infotainment systems, comparable to tablets and smartphones. Recently, several car manufacturers have announced the upcoming possibility to install third-party apps onto these infotainment systems. The prospect of running third-party code on a device that is integrated into a safety critical in-vehicle system raises serious concerns for safety, security, and user privacy. This paper investigates these concerns of in-vehicle apps. We focus on apps for the Android Automotive operating system which several car manufacturers have opted to use. While the architecture inherits much from regular Android, we scrutinize the adequateness of its security mechanisms with respect to the in-vehicle setting, particularly affecting road safety and user privacy. We investigate the attack surface and vulnerabilities for third-party in-vehicle apps. We analyze and suggest enhancements to such traditional Android mechanisms as app permissions and API control. Further, we investigate operating system support and how static and dynamic analysis can aid automatic vetting of in-vehicle apps. We develop AutoTame, a tool for vehicle-specific code analysis. We report on a case study of the countermeasures with a Spotify app using emulators and physical test beds from Volvo Cars

1001 Ways of Scenario Generation for Testing of Self-driving Cars: A Survey

Scenario generation is one of the essential steps in scenario-based testing and, therefore, a significant part of the verification and validation of driver assistance functions and autonomous driving systems. However, the term scenario generation is used for many different methods, e.g., extraction of scenarios from naturalistic driving data or variation of scenario parameters. This survey aims to give a systematic overview of different approaches, establish different categories of scenario acquisition and generation, and show that each group of methods has typical input and output types. It shows that although the term is often used throughout literature, the evaluated methods use different inputs and the resulting scenarios differ in abstraction level and from a systematical point of view. Additionally, recent research and literature examples are given to underline this categorization.Comment: accepted at IEEE IV 202

Simulating Vehicle Movement and Multi-Hop Connectivity from Basic Safety Messages

The Basic Safety Message (BSM) is a standardized communication packet that is sent every tenth of a second between connected vehicles using Dedicated Short Range Communication (DSRC). BSMs contain data about the sending vehicle's state, such as speed, location, and the status of the turn signal. Currently, many BSM datasets are available through the connected vehicle testbeds of U.S. Department of Transportation from all over the country. However, without a proper visualization tool, it is not possible to analyze or visually get an overview of the spatio-temporal distribution of the data. With this goal, a web application has been developed which can ingest a raw BSM dataset and display a time-based simulation of vehicle movement. The simulation also displays multi-hop vehicular network connectivity over DSRC. This paper gives details about the application, including an explanation of the multi-hop partitioning algorithm used to classify the vehicles into separate network partitions. A performance analysis for the simulation is included, in which it is suggested that calculating a connectivity matrix with the multi-hop partitioning algorithm is computationally expensive for large number of vehicles

New opportunities and perspectives for the electric vehicle operation in smart grids and smart homes scenarios

New perspectives for the electric vehicle (EV) operation in smart grids and smart homes context are presented. Nowadays, plugged-in EVs are equipped with on-board battery chargers just to perform the charging process from the electrical power grid (G2V – grid-to-vehicle mode). Although this is the main goal of such battery chargers, maintaining the main hardware structure and changing the digital control algorithm, the on-board battery chargers can also be used to perform additional operation modes. Such operation modes are related with returning energy from the batteries to the power grid (V2G- vehicle-to-grid mode), constraints of the electrical installation where the EV is plugged-in (iG2V – improved grid-tovehicle mode), interface of renewables, and contributions to improve the power quality in the electrical installation. Besides the contributions of the EV to reduce oil consumption and greenhouse gas emissions associated to the transportation sector, through these additional operation modes, the EV also represents an important contribution for the smart grids and smart homes paradigms. Experimental results introducing the EV through the aforementioned interfaces and operation modes are presented. An on-board EV battery charger prototype was used connected to the power grid for a maximum power of 3.6 kW.This work has been supported by COMPETE: POCI-01-0145-FEDER-007043 and FCT – Fundação para a Ciência e Tecnologia within the Project Scope: UID/CEC/00319/2013. This work is financed by the ERDF – European Regional Development Fund through the Operational Programme for Competitiveness and Internationalisation Ǧ COMPETE 2020 Programme, and by National Funds through the Portuguese funding agency, FCT - Fundação para a Ciência e a Tecnologia, within project SAICTPAC/0004/2015- POCI- 01-0145-FEDER-016434.info:eu-repo/semantics/publishedVersio

Energy Demand Prediction in Hybrid Electrical Vehicles for Speed Optimization

Targeting a resource-efficient automotive traffic, modern driver assistance systems include speed optimization algorithms to minimize the vehicle’s energy demand, based on predictive route data. Within these algorithms, the required energy for upcoming operation points has to be determined. This paper presents a model-based approach, to predict the energy demand of a parallel hybrid electrical vehicle, which is suitable to be used in speed optimization algorithms. It relies on separate models for the individual power train components, and is identified for a real test vehicle. On route sections of 5 to 7 km the averaged root mean square error for the state of charge prediction results to 0.91% while the required amount of fuel can be predicted with an averaged root mean square error of 0.05 liters